Early diagnosis of inherited metabolic diseases (IMDs) is important because treatment may lead to reduced mortality and improved prognosis. Due to their diversity, it is a challenge to diagnose IMDs in time, effecting an emerging need for a comprehensive test to acquire an overview of metabolite status. Untargeted metabolomics has proven its clinical potential in diagnosing IMDs, but is not yet widely used in genetic metabolic laboratories. We assessed the potential role of plasma untargeted metabolomics in a clinical diagnostic setting by using direct infusion high resolution mass spectrometry (DI-HRMS) in parallel with traditional targeted metabolite assays. We compared quantitative data and qualitative performance of targeted versus untargeted metabolomics in patients suspected of an IMD ( = 793 samples) referred to our laboratory for 1 year. To compare results of both approaches, the untargeted data was limited to polar metabolites that were analyzed in targeted plasma assays. These include amino acid, (acyl)carnitine and creatine metabolites and are suitable for diagnosing IMDs across many of the disease groups described in the international classification of inherited metabolic disorders (ICIMD). For the majority of metabolites, the concentrations as measured in targeted assays correlated strongly with the semi quantitative Z-scores determined with DI-HRMS. For 64/793 patients, targeted assays showed an abnormal metabolite profile possibly indicative of an IMD. In 55 of these patients, similar aberrations were found with DI-HRMS. The remaining 9 patients showed only marginally increased or decreased metabolite concentrations that, in retrospect, were most likely to be clinically irrelevant. Illustrating its potential, DI-HRMS detected additional patients with aberrant metabolites that were indicative of an IMD not detected by targeted plasma analysis, such as purine and pyrimidine disorders and a carnitine synthesis disorder. This one-year pilot study showed that DI-HRMS untargeted metabolomics can be used as a first-tier approach replacing targeted assays of amino acid, acylcarnitine and creatine metabolites with ample opportunities to expand. Using DI-HRMS untargeted metabolomics as a first-tier will open up possibilities to look for new biomarkers.
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http://dx.doi.org/10.3389/fmolb.2023.1283083 | DOI Listing |
J Am Soc Mass Spectrom
January 2025
Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida 32611, United States.
Reproducibility in untargeted metabolomics data processing remains a significant challenge due to software limitations and the complex series of steps required. To address these issues, we developed Nextflow4MS-DIAL, a reproducible workflow for liquid chromatography-mass spectrometry (LC-MS) metabolomics data processing, validated with publicly available data from MetaboLights (MTBLS733). Nextflow4MS-DIAL automates LC-MS data processing to minimize human errors from manual data handling.
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State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, China; Department of Head and Neck Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China. Electronic address:
Differentiated thyroid cancer (DTC) generally has a favorable prognosis, and radioactive iodine (RAI) therapy is typically used for metastatic DTC that continues to progress and poses life-threatening risks. However, resistance to RAI in metastatic DTC significantly impairs treatment effectiveness. This study aims to identify potential compounds that may influence RAI efficacy.
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Advanced Medical and Dental Institute, Universiti Sains Malaysia, Bertam 13200 Kepala Batas, Penang, Malaysia. Electronic address:
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Department of Medicine and Surgery, Proteomics and Metabolomics Unit, University of Milano-Bicocca, Vedano al Lambro 20854, Italy.
MALDI-HiPLEX-IHC mass spectrometry imaging (MSI) represents a newly established workflow to map tens of antibodies linked to photocleavable mass tags (PC-MTs), which report the distribution of antigens in formalin-fixed paraffin-embedded (FFPE) tissue sections. While this highly multiplexed approach has previously been integrated with untargeted methods, the possibility of mapping target cell antigens and performing bottom-up spatial proteomics on the same tissue section has yet to be explored. This proof-of-concept study presents a novel workflow combining MALDI-HiPLEX-IHC with untargeted spatial proteomics to analyze a single FFPE tissue section, using clinical clear cell renal cell carcinoma (ccRCC) tissue as a model.
View Article and Find Full Text PDFAlzheimers Dement
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Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China.
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